风险社会的来临与历史决定论的消解

风险社会的来临改变了人类的生存境遇和生活体验，也必将导致人们观念和行为方式的改变。对现代风险社会进行认真的反思，并将与“人为的不确定性”相联系的风险概念引入，可以消解历史决定论的思维定势，也可以为马克思历史唯物主义的真实意蕴及当代性的彰显提供全新的解释维度和现实根据。     

翻译:意识形态的建构、解构、重建与解读

翻译不仅是语言之间信息转换的文化活动,而且是一系列复杂的思维活动。意识形态以不同的语言形式隐藏在语篇之中,以各种隐蔽方式潜移默化地影响着作者写作、译者翻译以及读者解读文本。本文拟以批评语言学为主要方法论,强调翻译中语言分析和社会分析相结合的重要性,同时主张翻译研究应以语篇为单位,特别注意考察作者、译者、读者在翻译过程中的三元关系及其在意识形态的建构、解构、重建和解读过程中的作用,以便弄清翻译的本质并有效地解释那些"不忠"的现象。     

论建设农村全面小康社会的途径

文章将建设农村全面小康社会作为一项系统工程进行研究，既指出建设农村全面小康社会要依靠农业自身的发展，又阐述了从其他途径建设农村全面小康的可能性、必要性。     

三螺旋创新模式的理论探讨

基于非线性网状创新模型提出的“三螺旋场”和“三螺旋循环”概念进一步推进了三螺旋创新模式的理论研究。三螺旋场概念旨在解释在大学、产业和政府三股螺旋之间存在的相对独立和彼此作用的本质，说明三螺旋的生成原理、静态表现和动态演化特征。三螺旋的生成原理在于创新过程的非线性本质和多主体特征，静态表现为“内核外场模型”，而动态演化过程则在于纵向进化和横向循环。发生在三股螺旋之间的三螺旋循环揭示了在大学、产业和政府之间以人员、信息和产品流动为特征的相互作用和运行机制。     

濮城油田沙二上1砂组末端扇沉积体系研究

曲流河、辫状河、网状河和若干三角洲的沉积模式目前已成为对比和解释古河流岩石层序的标准框架。但是，近来的野外资料和钻井资料证明，应该把末端扇模式加入上述沉积模式表中。末端扇的作用、地貌、有关的沉积物及沉积学特征相比河流体系及三角洲体系差距较大。通过大量的岩芯观察和描述，结合测井资料、区域地质资料及各项分析鉴定资料等进行综合分析，对东濮凹陷濮城油田沙二上亚段1砂组的沉积特征与沉积相进行了较全面的分析，认为该地区主要为末端扇沉积体系，在对末端扇的岩性、沉积相序、沉积微相特征分析的基础上，研究了末端扇的识别标志并建立了末端扇的沉积模式。     

Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix

Approximation formulae are developed for the bias of ordinary and generalized Least Squares Dummy Variable (LSDV) estimators in dynamic panel data models. Results from Kiviet [Kiviet, J. F. (1995), on bias, inconsistency, and efficiency of various estimators in dynamic panel data models, J. Econometrics68:53-78; Kiviet, J. F. (1999), Expectations of expansions for estimators in a dynamic panel data model: some results for weakly exogenous regressors, In: Hsiao, C., Lahiri, K., Lee, L-F., Pesaran, M. H., eds., Analysis of Panels and Limited Dependent Variables, Cambridge: Cambridge University Press, pp. 199-225] are extended to higher-order dynamic panel data models with general covariance structure. The focus is on estimation of both short- and long-run coefficients. The results show that proper modelling of the disturbance covariance structure is indispensable. The bias approximations are used to construct bias corrected estimators which are then applied to quarterly data from 14 European Union countries. Money demand functions for M1, M2 and M3 are estimated for the EU area as a whole for the period 1991: I-1995: IV. Significant spillovers between countries are found reflecting the dependence of domestic money demand on foreign developments. The empirical results show that in general plausible long-run effects are obtained by the bias corrected estimators. Moreover, finite sample bias, although of moderate magnitude, is present underlining the importance of more refined estimation techniques. Also the efficiency gains by exploiting the heteroscedasticity and cross-correlation patterns between countries are sometimes considerable.     

Generalized Laplacian approximations in Bayesian inference

This paper presents a new Laplacian approximation to the posterior density of η = g(θ). It has a simpler analytical form than that described by Leonard et al. (1989). The approximation derived by Leonard et al. requires a conditional information matrix R_η to be positive definite for every fixed η. However, in many cases, not all R_η are positive definite. In such cases, the computations of their approximations fail, since the approximation cannot be normalized. However, the new approximation may be modified so that the corresponding conditional information matrix can be made positive definite for every fixed η. In addition, a Bayesian procedure for contingency-table model checking is provided. An example of cross-classification between the educational level of a wife and fertility-planning status of couples is used for explanation. Various Laplacian approximations are computed and compared in this example and in an example of public school expenditures in the context of Bayesian analysis of the multiparameter Fisher-Behrens problem.     

Low dose risk estimation via simultaneous statistical inferences

Summary.  The paper develops and studies simultaneous confidence bounds that are useful for making low dose inferences in quantitative risk analysis. Application is intended for risk assessment studies where human, animal or ecological data are used to set safe low dose levels of a toxic agent, but where study information is limited to high dose levels of the agent. Methods are derived for estimating simultaneous, one-sided, upper confidence limits on risk for end points measured on a continuous scale. From the simultaneous confidence bounds, lower confidence limits on the dose that is associated with a particular risk (often referred to as a bench-mark dose ) are calculated. An important feature of the simultaneous construction is that any inferences that are based on inverting the simultaneous confidence bounds apply automatically to inverse bounds on the bench-mark dose.     

Statistics for weighing benefits and harms in a proposed genetic substudy of a randomized cancer prevention trial

Summary.  When evaluating potential interventions for cancer prevention, it is necessary to compare benefits and harms. With new study designs, new statistical approaches may be needed to facilitate this comparison. A case in point arose in a proposed genetic substudy of a randomized trial of tamoxifen versus placebo in asymptomatic women who were at high risk for breast cancer. Although the randomized trial showed that tamoxifen substantially reduced the risk of breast cancer, the harms from tamoxifen were serious and some were life threaten-ing. In hopes of finding a subset of women with inherited risk genes who derive greater bene-fits from tamoxifen, we proposed a nested case–control study to test some trial subjects for various genes and new statistical methods to extrapolate benefits and harms to the general population. An important design question is whether or not the study should target common low penetrance genes. Our calculations show that useful results are only likely with rare high penetrance genes.     

Generalized additive models for location, scale and shape

Summary.  A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects. The distribution for the response variable in the GAMLSS can be selected from a very general family of distributions including highly skew or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only of the mean (or location) but also of the other parameters of the distribution of y , as parametric and/or additive nonparametric (smooth) functions of explanatory variables and/or random-effects terms. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models. A Newton–Raphson or Fisher scoring algorithm is used to maximize the (penalized) likelihood. The additive terms in the model are fitted by using a backfitting algorithm. Censored data are easily incorporated into the framework. Five data sets from different fields of application are analysed to emphasize the generality of the GAMLSS class of models.